Postdoc: Quantum Materials & Topology (DFT/ML) – Onsite
Los Alamos National Laboratory
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Job summary
Los Alamos National Laboratory (LANL) invites applications for a postdoctoral position in computational materials physics within the Quantum and Condensed Matter Physics Group. The candidate will apply first-principles DFT (VASP/QE/Wien2k) and high-throughput workflows, enhanced with machine learning, to discover and inversely design 2D f-electron topological materials.
The work will combine workflow development with applying existing codes, and it will involve collaboration with LANL and UC
Qualifications
- Strong publications record in computational materials theory.
- Dense-DFT experience with standard codes (VASP, QE, Wien2k).
- Programming skills in Python, C++, or Fortran.
- Experience with high-performance Linux environments.
- Familiarity with machine learning is desirable.
- Ability to work both collaboratively and independently.
Responsibilities
- Develop computational workflows and apply DFT codes to discover 2D f-electron topological materials.
- Collaborate with theory and experimental teams to advance quantum information goals.
Skills
Education
Tools
Job description
Los Alamos National Laboratory (LANL) invites applications for a postdoctoral position in computational materials physics within the Quantum and Condensed Matter Physics Group. The candidate will apply first-principles DFT (VASP/QE/Wien2k) and high-throughput workflows, enhanced with machine learning, to discover and inversely design 2D f-electron topological materials.
The work will combine workflow development with applying existing codes, and it will involve collaboration with LANL and UC
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